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<title>Creating tensors</title>

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<h1 class="title toc-ignore">Creating tensors</h1>



<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(torch)</span></code></pre></div>
<p>In this article we describe various ways of creating <code>torch</code> tensors in R.</p>
<div id="from-r-objects" class="section level2">
<h2>From R objects</h2>
<p>You can create tensors from R objects using the <code>torch_tensor</code> function. The <code>torch_tensor</code> function takes an R vector, matrix or array and creates an equivalent <code>torch_tensor</code>.</p>
<p>You can see a few examples below:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="fu">torch_tensor</span>(<span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>))</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  2</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  3</span></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPUFloatType{3} ]</span></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a><span class="co"># conform to row-major indexing used in torch</span></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a><span class="fu">torch_tensor</span>(<span class="fu">matrix</span>(<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>, <span class="at">ncol =</span> <span class="dv">5</span>, <span class="at">nrow =</span> <span class="dv">2</span>, <span class="at">byrow =</span> <span class="cn">TRUE</span>))</span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   1   2   3   4   5</span></span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   6   7   8   9  10</span></span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPULongType{2,5} ]</span></span>
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a><span class="fu">torch_tensor</span>(<span class="fu">array</span>(<span class="fu">runif</span>(<span class="dv">12</span>), <span class="at">dim =</span> <span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">3</span>)))</span>
<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; (1,.,.) = </span></span>
<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   0.8004  0.7253  0.9551</span></span>
<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   0.5068  0.3267  0.8677</span></span>
<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; </span></span>
<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; (2,.,.) = </span></span>
<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   0.8755  0.1164  0.0132</span></span>
<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;   0.6977  0.9201  0.9417</span></span>
<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPUFloatType{2,2,3} ]</span></span></code></pre></div>
<p>By default, we will create tensors in the <code>cpu</code> device, converting their R datatype to the corresponding torch <code>dtype</code>.</p>
<blockquote>
<p><strong>Note</strong> currently, only numeric and boolean types are supported.</p>
</blockquote>
<p>You can always modify <code>dtype</code> and <code>device</code> when converting an R object to a torch tensor. For example:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">torch_tensor</span>(<span class="dv">1</span>, <span class="at">dtype =</span> <span class="fu">torch_long</span>())</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1</span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPULongType{1} ]</span></span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="fu">torch_tensor</span>(<span class="dv">1</span>, <span class="at">device =</span> <span class="st">&quot;cpu&quot;</span>, <span class="at">dtype =</span> <span class="fu">torch_float64</span>())</span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1</span></span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPUDoubleType{1} ]</span></span></code></pre></div>
<p>Other options available when creating a tensor are:</p>
<ul>
<li><code>requires_grad</code>: boolean indicating if you want <code>autograd</code> to record operations on them for automatic differentiation.</li>
<li><code>pin_memory</code>: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.</li>
</ul>
<p>These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.</p>
</div>
<div id="using-creation-functions" class="section level2">
<h2>Using creation functions</h2>
<p>You can also use the <code>torch_*</code> functions listed below to create torch tensors using some algorithm.</p>
<p>For example, the <code>torch_randn</code> function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the <code>...</code> argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">torch_randn</span>(<span class="dv">5</span>, <span class="dv">3</span>)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>x</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; -0.1352 -0.3267 -0.4535</span></span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; -1.6530 -0.1420  0.5372</span></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  0.4575 -0.9364  0.1152</span></span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; -1.5834 -1.5565  0.8754</span></span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; -0.0757 -0.0130  0.5759</span></span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPUFloatType{5,3} ]</span></span></code></pre></div>
<p>Another example is <code>torch_ones</code>, which creates a tensor filled with ones.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">torch_ones</span>(<span class="dv">2</span>, <span class="dv">4</span>, <span class="at">dtype =</span> <span class="fu">torch_int64</span>(), <span class="at">device =</span> <span class="st">&quot;cpu&quot;</span>)</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>x</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1  1  1  1</span></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1  1  1  1</span></span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPULongType{2,4} ]</span></span></code></pre></div>
<p>Here is the full list of functions that can be used to bulk-create tensors in torch:</p>
<ul>
<li><code>torch_arange</code>: Returns a tensor with a sequence of integers,</li>
<li><code>torch_empty</code>: Returns a tensor with uninitialized values,</li>
<li><code>torch_eye</code>: Returns an identity matrix,</li>
<li><code>torch_full</code>: Returns a tensor filled with a single value,</li>
<li><code>torch_linspace</code>: Returns a tensor with values linearly spaced in some interval,</li>
<li><code>torch_logspace</code>: Returns a tensor with values logarithmically spaced in some interval,</li>
<li><code>torch_ones</code>: Returns a tensor filled with all ones,</li>
<li><code>torch_rand</code>: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).</li>
<li><code>torch_randint</code>: Returns a tensor with integers randomly drawn from an interval,</li>
<li><code>torch_randn</code>: Returns a tensor filled with values drawn from a unit normal distribution,</li>
<li><code>torch_randperm</code>: Returns a tensor filled with a random permutation of integers in some interval,</li>
<li><code>torch_zeros</code>: Returns a tensor filled with all zeros.</li>
</ul>
</div>
<div id="conversion" class="section level2">
<h2>Conversion</h2>
<p>Once a tensor exists you can convert between <code>dtype</code>s and move to a different device with <code>to</code> method. For example:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">torch_tensor</span>(<span class="dv">1</span>)</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> x<span class="sc">$</span><span class="fu">to</span>(<span class="at">dtype =</span> <span class="fu">torch_int32</span>())</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>x</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1</span></span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>y</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; torch_tensor</span></span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt;  1</span></span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a><span class="co">#&gt; [ CPUIntType{1} ]</span></span></code></pre></div>
<p>You can also copy a tensor to the GPU using:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>x <span class="ot">&lt;-</span> <span class="fu">torch_tensor</span>(<span class="dv">1</span>)</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>y <span class="ot">&lt;-</span> x<span class="sc">$</span><span class="fu">cuda</span>()<span class="er">)</span></span></code></pre></div>
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